Breast cancer, by far the leading type of cancer incidence in women, causes about 170,000 new cases a year, more than double the amount caused by colorectal cancer, the second major type in women. However, early diagnosis and treatment of breast cancer provide one of the highest chances of survival among cancer types in women. The American Cancer Society recommends a yearly mammogram examination for asymptomatic women over the age of 35 and Medicare covers these procedures.
Awareness and willingness for prevention of breast cancer is rapidly increasing in the general-public. Therefore, it is possible that mammography will soon be one of the highest volume X-ray procedures regularly used in radiology clinics. The increasing burden on radiologists is being experienced at many medical centers. A reliable computerized system can contribute both speed and accuracy to mammogram interpretation.
The first and sometimes the only mammographic sign in early, curable breast cancer is a cluster of microcalcifications that are visible in about 50% of breast cancer cases. Microcalcifications typically have a higher X-ray opacity than that of normal breast tissue and they appear as relatively brighter structures ranging from 0.1 mm to 2 mm in width in a mammogram. In visual inspection, one cluster of microcalcifications consists of 3 or more individual microcalcifications that appear in an area of about 1 cm.sup.2.
Due to the subtlety of some microcalcifications, visual interpretation of a mammogram is a tedious process that generally requires a magnifying glass, and that, in some cases, can take more than 15 minutes. In visual inspection, the probability of false negatives is high and a significant level of false positives is reported, i.e., only one out of five cases that radiologists interpret as potential cancer is confirmed in a biopsy examination.
The factors that contribute to the difficulty of visually recognizing microcalcifications are their small size; their morphological variability; their similarity to other microstructures that are unrelated to cancer, e.g., film artifacts, lead shot positioning markers, and some benign tissue structures; and the relatively low contrast of mammograms.
For an automated system, the small size of microcalcifications does not pose a large problem because digitization resolutions (e.g. 25 microns/pixel) that provide adequate information on the smallest microcalcifications are available. However, the other three factors present challenges that successful automated systems have to meet.
Previously developed automated detection techniques reported varying levels of performance with different algorithms. See, H-P Chan et al., "Computer-aided detection of microcalcifications in mammograms: methodology and preliminary clinical study," Invest Radiol., vol. 23, p. 664, 1988; B. W. Fam et al., "Algorithm for the detection of fine clustered calcifications on film mammograms, " Radiology, vol 169, p. 333, 1988; and D. H. Davies and D. R. Dance, "Automatic computer detection of clustered calcifications in digital mammograms, " Phys. Med Biol., vol 35, p. 1111, 1990.
Chan's approach is based on a heuristic signal enhancement filter and local threshold crossing detection and was able to detect 90% of the microcalcification clusters along with a few false positive clusters per mammogram. As Chan states in the conclusion of his article, the detection accuracy was lower than that of an average radiologist, and the radiologist had to rule out the large number of false positives. Better results were reported by Fam and Davies.
The potential difficulties and pitfalls of available automated detection techniques can be summarized as follows:
a. Too little enhancement may preclude the detection of minor microcalcification peaks while too much enhancement may increase significantly the amplitude of small background structures (noise) and thus produce a large number of false detections. An acceptable compromise may not exist in some images, and in those images where it exists, it can change from image to image and can be difficult to determine. PA1 b. A small, square region of analysis (moving kernel) where operational parameters are computed, may be inappropriate for the natural shape of microcalcifications and automated detection based on such approaches may depart considerably, in some cases, from the outcome of visual detection. PA1 c. A large number of parameters whose values have to be entered manually (e.g., Fam) is not a viable approach for expedient clinical use. PA1 a. Operation on raw data (no enhancement) to ensure that both visual interpretation and automated detection use the same information. PA1 b. Approach that is compatible with the natural morphology of microcalcifications; no use of small square areas of interest or moving kernels. PA1 c. A minimal number of operational parameters that can be set adaptively and automatically for any image, allowing fully automated operation. PA1 d. Visual interpretability of operational parameters.
Considering the limitations of the methods discussed above, any new detection method has to meet the following requirements:
The above considerations and the unsatisfactory results obtained with some of the available detection techniques, led to the development of the fundamentally different detection method described and claimed herein.